论文标题
使用嘈杂的相对测量值对多个目标进行主动定位
Active localization of multiple targets using noisy relative measurements
论文作者
论文摘要
考虑一个通过获得相对测量值在未知位置本地化目标的移动机器人。观察值可以是轴承或范围测量。机器人应该如何移动以将目标定位并尽快将其位置的不确定性最小化？大多数现有的方法本质上是贪婪的，要么依赖于准确的初始估计。 我们将这个路径计划问题提出为无监督的学习问题，其中测量使用贝叶斯直方图滤波器进行了汇总。机器人学会使用当前的测量值和当前信仰状态的总体表示，在最短的时间内将每个目标的总不确定性最小化。我们在一系列实验中分析了我们的方法，我们表明我们的方法表现优于标准贪婪方法。此外，它的性能也可与具有访问目标真实位置的离线算法相媲美。
Consider a mobile robot tasked with localizing targets at unknown locations by obtaining relative measurements. The observations can be bearing or range measurements. How should the robot move so as to localize the targets and minimize the uncertainty in their locations as quickly as possible? Most existing approaches are either greedy in nature or rely on accurate initial estimates. We formulate this path planning problem as an unsupervised learning problem where the measurements are aggregated using a Bayesian histogram filter. The robot learns to minimize the total uncertainty of each target in the shortest amount of time using the current measurement and an aggregate representation of the current belief state. We analyze our method in a series of experiments where we show that our method outperforms a standard greedy approach. In addition, its performance is also comparable to an offline algorithm which has access to the true location of the targets.
